Association between Lifestyle Habits Questionnaire and Plasma Free Amino Acid Profile in Japanese Rural Community Dwellers
Recently, accumulating evidence has revealed that the concentrations of plasma free amino acids (PFAAs) are potential biomarkers for overnutrition and subsequent insulin resistance [1-3], and/or protein malnutrition [4-6]. Among the PFAAs, especially branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs), are associated with visceral obesity, insulin resistance, and diabetes mellitus in several cross-sectional and prospective cohort studies [1,7-10]. BCAA concentrations are elevated in obese humans and animal models [9,11,12]. This elevation is caused by insulin resistance which decreases utilization of amino acids and uptake of BCAAs into muscles [13,14]. Furthermore, insulin resistance decreases expression of adipose-tissue BCAA catabolizing enzymes, leading to decreased BCAA metabolism in visceral adipose tissue [13,15,16]. Other plasma free amino acid concentrations are also altered in people with high visceral obesity ; alanine (Ala), glutamate (Glu), phenylalanine (Phe), proline (Pro), tryptophan (Trp) and tyrosine (Tyr) are elevated, while glycine (Gly) and serine (Ser) are decreased. It is believed that this alteration is caused by a combination of insulin resistance-induced accelerated protein break down in muscle and changes in the gluconeogenesis set point in liver.
point in liver. On the other hand, insufficient protein intake could trigger low concentrations of essential and semi-essential amino acids in blood [4,5]. Insufficient protein intake, which is called protein malnutrition, is common across varying populations, particularly in elderly subjects. It has been associated with increased risk of sarcopenia, heart failure, impaired immune response, impaired respiratory function, delayed wound healing, overall increased complications and increased mortality in various populations [17-19]. Especially, the importance of ingesting enough amount of protein is demonstrated in the elderly population . Furthermore, in generally healthy population who go to annual health checkup, low essential and semi-essential amino acid concentrations in blood was significantly associated with the protein malnutrition-associated markers, anemia-associated markers, sympathetic nerve activity-associated markers, and inflammation and immune function-associated markers [6,21]
Although, the measurement of PFAAs to evaluate the risk of overnutrition and protein malnutrition is potentially useful, social implementation involves several difficulties. The fasting blood has to be collected in the morning in hospitals with strict sample management to keep metabolites stable, and the blood has to be transported to the site where centrifugation can be done for plasma preparation. The rigorous measurement of PFAAs requires high-performance liquid chromatography– electrospray ionization mass spectrometry followed by precolumn derivatization [21-26]. Thus, we hypothesized that if the results of a simple questionnaire for lifestyle habits could be correlated to PFAA concentrations, then this could be used as an initial screening tool for PFAA alterations.
In this study, we designed a lifestyle habits questionnaire associated with overnutrition and protein malnutrition, suitable for Japanese rural community dwellers. And then, we quantified PFAA concentrations and investigated the association with the results of lifestyle habits questionnaire in 1,764 participants.
MATERIALS AND METHODS
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Saihaku Hospital (Tottori, Japan). A total of 2,809 Japanese participants who visited Saihaku Hospital from January 2012 to June 2014 were enrolled. All participants were generally healthy and at least 20 years of age (mean age: 62.9 ± 11.1 years) (Table 1). Exclusion criteria included pregnancy, severe mental disorders, and cancer
The lifestyle habits questionnaire
The lifestyle habits questionnaire with 19 items was used to assess the dietary habits, alcohol consumption, sleeping habits, physical activity and body weight change (Table 2) [27-37], which was optimized for the lifestyle of Japanese rural community dwellers. Q.1, Q.2, Q.3, Q.4, Q.5, Q.6, and Q.7 are related with overnutrition lifestyle habits, while Q.15, Q.16, Q.17, Q.18 and Q.19 are related with protein malnutrition lifestyle. Q. 8, Q. 9, Q.10, Q. 11, Q. 12, Q. 13 and Q. 14 are related with sleep duration, frequency to intake dairy food, snack habit, gait speed, fruit and vegetable intake. The questionnaire survey was conducted via mail from August 2014 to September 2014. The participants answered using two scales (Yes/No) to the each question. Finally, 1,764 participants (62.8% response rate) delivered a completed questionnaire.
Measurement of plasma free amino acid concentrations
At Saihaku Hospital, blood samples (5 mL) were collected from forearm veins after overnight fasting into tubes containing disodium ethylenediaminetetraacetate (2Na·EDTA) that were immediately placed on ice. Plasma was prepared by centrifugation at 3,000 rpm at 4 for 15min. The plasma amino acid concentrations were measured by high-performance liquid chromatography–electrospray ionization mass spectrometry followed by precolumn derivatization as previously described [22-26]. The following 21 amino acids were measured: Ala, arginine (Arg), asparagine (Asn), citrulline (Cit), Glu, glutamine (Gln), Gly, histidine (His), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), ornithine (Orn), Phe, Pro, Ser, threonine (Thr), tryptophan (Trp), Tyr, valine (Val), and α-Amino butyric acid (αABA).
The statistical analyses except for the estimation of linear regression models were performed using the JMP 13.2.1 program (SAS Institute Inc., Cary, NC, USA). Welch’s t-tests were used to compare male and female. Categorical data were analyzed using chi-square test. The data in the tables are expressed as the mean ± SE.
Relationship between plasma free amino acid concentrations and answer of each question (Yes = 1, No = 0) was evaluated in terms of sex-adjusted standardized partial regression coefficients. The R language (R version 3.2.4 Revised, http://www.r-project. org/) was implemented for the estimation of linear regression models. Statistical significance was set at P<0.05.
RESULTS AND DISCUSSION
ESULTS AND DISCUSSION We generated the lifestyle habits questionnaire which is suitable for Japanese rural community dwellers to evaluate the overnutrition and protein malnutrition. This study demonstrated the questions regarding overnutrition and protein malnutrition had association with some of PFAA profiles in 1,764 Japanese participants, and thus, it could be a potential screening tool to evaluate the PFAA profiles.
Table (1) represents the demographics and plasma free amino acid profiles of the participants. The plasma amino acid concentrations were significantly higher in male than in female, except for Gly and Ser, and these concentrations were within the reference intervals for plasma amino acid concentrations in generally healthy Japanese subjects . Table (2) shows the lifestyle habits questionnaire, and Table (3) indicates the number and percent of participants who responded “Yes” to each question. The ratio of the participants who responded “Yes” to Q.1, Q.5, Q.9, Q.12, Q.13 and Q.18 were significantly higher in male than female, while Q.7 and Q.10 were significantly lower in male. Decrease in PFAA concentrations might be causality or result of loss of appetite. Further clarification would be needed
There are two limitations in this study. The populations in this study were community dwellers who lived in a rural Japanese area and most of them are elderly. This specific population and locality could be a bias for the result. Some questions regarding sleep duration, frequency to intake dairy food, snack habit, gait speed, fruit and vegetable intake such as Q. 8, Q. 9, Q. 10, Q. 11, Q. 12, Q. 13 and Q. 14 had little impact to PFAA profiles. This might be related to the specific lifestyle of Japanese rural community dwellers. Comparison with other populations with different ages and areas are necessary. The second limitation is lack of other blood biochemical variables, medical information, and rigorous nutritional intake records in these participants. In this study, only plasma amino acid concentrations and answers to the questionnaire were available. Although the multiple analyses performed in the study demonstrated a significant relationship between PFAAs and lifestyle questionnaire, further clarification how other possible factors affected the results are to be demonstrated in the future.
Table 1: Demographic and plasma free amino acid concentrations.
|Age (y)||66.0 ± 0.4||64.6 ± 0.3|
|Amino acids (μmol/L|
|αABA||20.1 ± 0.2||18.4 ± 0.2||***|
|Ala||361.6 ± 2.8||308.8 ± 2.2||***|
|Arg||101.2 ± 0.6||92.8 ± 0.5||***|
|Asn||47.2 ± 0.2||42.9 ± 0.2||***|
|Cit||33.7 ± 0.3||31.3 ± 0.2||***|
|Gln||602.4 ± 2.7||585.8 ± 2.0||***|
|Glu||32.0 ± 0.5||23.7 ± 0.3||***|
|Gly||208.1 ± 1.6||237.0 ± 2.2||***|
|His||80.8 ± 0.3||76.0 ± 0.3||***|
|Ile||66.0 ± 0.5||50.3 ± 0.3||***|
|Leu||128.5 ± 0.7||102.8 ± 0.5||***|
|Lys||202.5 ± 1.1||183.9 ± 0.8||***|
|Met||27.2 ± 0.2||22.6 ± 0.1||***|
|Orn||53.7 ± 0.5||48.5 ± 0.4||***|
|Phe||60.4 ± 0.3||53.4 ± 0.2||***|
|Pro||149.0 ± 1.5||117.4 ± 1.1||***|
|Ser||107.6 ± 0.6||111.0 ± 0.7||***|
|Thr||130.3 ± 1.0||115.6 ± 0.8||***|
|Trp||57.1 ± 0.3||49.5 ± 0.2||***|
|Tyr||66.5 ± 0.4||57.9 ± 0.3||***|
|Val||230.6 ± 1.3||195.3 ± 1.0||***|
The continuous variables are summarized as means ± standard error (SE). Significant differences between male and female are shown as *P<0.05, **P<0.01, ***P<0.001 according to Welch's t-test
Table 2: Lifestyle habits questionnaire.
|Q.1||I have gained more than 10 kg of body weight compared with 20 years ago (or when I was 18 years old).|
|Q.2||I usually eat fast and do not stop eating until I become full|
|Q.3||I eat most of the meal at dinner or have a habit of eating a midnight snack|
|Q.4||I try to limit the amount to eat.|
|Q.5||I often eat fatty meat such as bacon and sausage.|
|Q.6||I often eat meals less than 2 times a day.|
|Q.7||I tend to sit down rather than get up and work during free time, or I do not like exercise.|
|Q.8||I sleep for less than 5 hours a day|
|Q.9||I eat dairy food and drink milk less than 3 times a week.|
|Q.10||I have a habit of eating snacks|
|Q.11||I walk slower compared with others who are in the same sex and the same generations.|
|Q.12||I rarely eat fruits|
|Q.13||I eat vegetables less than 350g or 5 dishes a day. *1 dish corresponds a small bowl or a side dish|
|Q.14||As a staple food, I eat refined grains such as white rice or white bread rather than unrefined brown rice. (Refined grains include white rice, white bread, udon, and ramen noodle, while unrefined grains include brown rice, rye, millet bread, and buckwheat noodles.)|
|Q.15||On average, I eat meats less than 1 time a day.|
|Q.16||On average, I eat seafood less than 1 time a day.|
Table 3: The percent of subjects who answered “Yes” to the lifestyle questionnaire.
|All N (%)||Male N (%)||Female N (%)||P||All N (%)||Male N (%)||Female N (%)||P|
|Q.1||551 (31.5%)||297 (39.7%)||254 (25.3%)||***||Q.11||486 (27.7%)||215 (28.6%)||271 (27.0%)|
|Q.2||482 (27.6%)||209 (27.8%)||273 (27.4%)||Q.12||626 (35.8%)||326 (43.6%)||300 (29.9%)||***|
|Q.3||211 (12.1%)||81 (10.9%)||130 (13.1%)||Q.13||1057 (60.5%)||486 (65.1%)||571 (57.2%)||***|
|Q.4||539 (30.7%||218 (29.1%)||321 (32.0%)||Q.14||1516 (86.5%)||648 (86.4%)||868 (86.6%)|
|Q.5||311 (17.8%)||170 (22.6%)||141 (14.1%)||***||Q.15||1188 (68.1%)||525 (69.8%)||663 (66.8%)|
|Q.6||127 (7.2%)||63 (8.4%)||64 (6.4%)||Q.16||884 (50.6%)||372 (49.5%)||512 (51.5%)|
|Q.7||671 (38.4%)||263 (35.0%)||408 (41.0%)||*||Q.17||119 (6.8%)||49 (6.5%)||70 (7.0%)|
|Q.8||232 (13.2%)||93 (12.4%)||139 (13.9%)||Q.18||234 (13.5%)||187 (25.3%)||47 (4.7%)||***|
|Q.9||502 (28.6%)||275 (36.5%)||227 (22.7%)||***||Q.19||62 (3.5%)||19 (2.5%)||43 (4.3%)||0.0503|
|Q.10||924 (52.8%)||304 (40.4%)||620 (62.1%)||***|
The categorical variables are shown as frequencies and proportions. Significant differences between male and female are shown as *P<0.05, **P<0.01, ***P<0.001 according to chi-square test.
Table 4: Sex-adjusted P-values of standardized partial regression coefficients between each PFAA concentration and the lifestyle-related question.
Significant association (P<0.01) between the lifestyle-related question and each PFAA are highlighted in grey.
In conclusion, the present study confirmed the association between the results of a lifestyle habits questionnaire and PFAA profiles. For simple checkup for PFAA alterations, this questionnaire could be a possible initial screening tool for Japanese rural community dwellers
The authors’ responsibilities were as follows: KN, TA and OK designed the research; HJ, KN, YK, TM, MM and OK conducted the research; HJ, KN and AI analyzed the data; HJ, KN and TK wrote the paper; and TK had the primary responsibility for the final content. All the authors read and approved the final manuscript.
CONFLICT OF INTEREST
OK received research grants from Ajinomoto Co., Inc. HJ, KN, AI, YK, TM, MM and TA are employees of Ajinomoto Co., Inc. TK is a board member of Ajinomoto Co., Inc. The entire financial source was provided by Ajinomoto Co., Inc. No other potential conflicts of interest relevant to this article are declared.
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The plasma free amino acid (PFAA) profiles are demonstrated to be altered by overnutrition and subsequent insulin resistance, and/or protein malnutrition in generally healthy subjects. Although, the measurement of PFAAs to evaluate the risk of overnutrition and protein malnutrition is potentially useful, large scale social implementation present several difficulties. Currently, rigorous measurement of PFAAs using high-performance liquid chromatography–electrospray ionization mass spectrometry, following the collection of fasting blood in the morning in hospitals with strict sample management, is required, which can be a burden for local hospitals. In this study, we designed a simple lifestyle habits questionnaire consisting of 19 questions reported to be associated with overnutrition and protein malnutrition, suitable for Japanese rural communities. And then, we investigated the association between PFAAs and the results of the questionnaire in 1,764 Japanese local community dwellers. The results of questions regarding lifestyles leading to overnutrition and subsequent body weight gain were associated with higher concentrations of most essential amino acids including branched chain amino acids and aromatic amino acids. On the other hand, results of questions related to lifestyles leading to protein malnutrition were associated with lower concentrations of some essential amino acids. The results of questions regarding sleep duration, frequency of dairy food intake, snack habit, gait speed, fruit and vegetable intake had little impact on PFAA profiles. This simple lifestyle habits questionnaire could be a potential screening tool to predict alterations of PFAA profiles. Further validations of the associations with other populations are necessary before large scale social implementation.
• Plasma free amino acid • Lifestyle habits • Overnutrition • Protein malnutrition
PFAAs: Plasma Free Amino Acids; BCAAs: Branched-Chain Amino Acids; AAAs: Aromatic Amino Acids; Ala: Alanine; Arg: Arginine; Asn: Asparagine; Cit: Citrulline; Glu: Glutamate; Gln: Glutamine; Gly: Glycine; His: Histidine; Ile: Isoleucine; Leu: Leucine; Lys: Lysine; Met: Methionine; Orn: Ornithine; Phe: Phenylalanine; Pro: Proline; Ser: Serine; Thr: Threonine; Trp: Tryptophan; Tyr: Tyrosine; Val: Valine; αABA: α-Amino Butyric Acid; SE: Standard Error; SPRC: Standardized Partial Regression Coefficients; 2Na·EDTA: Disodium Ethylenediaminetetraacetate